The goal of this project is to enhance shared decision-making (SDM) between Veterans and healthcare providers. We hypothesize that we can empower patients to participate in SDM by providing graphical information regarding outcomes of interest along with concrete stories about individuals like themselves. Currently, healthcare systems are striving to engage patients as partners in order to make healthcare more patient-centered. There is strong evidence that shared decision-making (SDM) improves both patient satisfaction and health outcomes. In order for patients to actively participate in SDM, they need to be informed of options' pros and cons. Despite the availability of information from multiple sources, many patients do not have this type of information available. Consider the following example: Jim, a 56 year-old Veteran with a history of hypertension, presents to his primary care provider with a complaint of palpitations and fatigue. An ECG demonstrates atrial fibrillation. He is started on metoprolol for rate control with resolution of his symptoms. During a brief discussion about options for thromboembolic risk reduction therapy, his doctor says other medications don't reduce your risk of stroke enough and are too new, so Jim is started on warfarin. After several weeks of repeated lab draws for INR monitoring and struggling with dietary modification, Jim wishes that he had been provided with more information about the outcomes and experiences of patients like him on the different therapies for anticoagulation. We believe a new type of decision aid that safely puts the power of a large clinical data repository into the hands of patients could help meet the information needs for SDM in the example above. Patient decision aids improve people's knowledge of options, create accurate perceptions of benefits and harms, reduce difficulty in decision-making, and increase participation in the decision-making process. Current patient decision aids have limitations including: 1) the need for experts to summarize the medical evidence; 2) the labor required to produce frequent updates; 3) the limitations and contradictions of the medical evidence. In addition, few decision aids provide stories about patients in similar situations. To address these limitations, we envision an extendable and engaging patient decision aid called VeteransLikeMe (VLMe). VLMe will retrieve relevant cases from a clinical repository and use interactive graphic displays to communicate summary statistics and automatically compose patient stories to facilitate patient engagement. Our vision is to use big clinical data to help inform patients. Since this type of decision aid has not been previously made available to patients and will take considerable resources to develop, we propose to first carry out a pilot study. In this pilot study, we will conduct a needs analysis to inform the design of VLMe, develop a mockup interface, and test the acceptability, feasibility and safety of combining a graphical representation of outcome information along with stories about similar patients in the context of SDM. The pilot will focus on medication management decisions for atrial fibrillation (AF), a highly prevalent condition in the VA patient population. We will address the following question: How can Veterans be informed of the relative benefits and potential side effects of different medication strategies in a way that is personally meaningful and supports involvement in therapeutic decision-making? Specific Aim 1: Analyze the SDM needs of Veterans with AF for anticoagulation therapy decisions. Specific Aim 2: Create a mockup visual display and vignettes to communicate outcomes of interest . Specific Aim 3: Conduct a trial of the mockup VLMe to assess acceptability, feasibility and safety.